Time series forecasting with scikit-learn regressors.
Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM, XGBoost, Ranger...).
Documentation: https://joaquinamatrodrigo.github.io/skforecast/
pip install skforecast
Specific version:
pip install skforecast==0.4.3
Latest (unstable):
pip install git+https://github.com/JoaquinAmatRodrigo/skforecast#master
The most common error when importing the library is:
'cannot import name 'mean_absolute_percentage_error' from 'sklearn.metrics'
.
This is because the scikit-learn installation is lower than 0.24. Try to upgrade scikit-learn with:
pip3 install -U scikit-learn
- numpy>=1.20, <=1.22
- pandas>=1.2, <=1.4
- tqdm>=4.57.0, <=4.62
- scikit-learn>=1.0, <=1.1.1
- statsmodels>=0.12, <=0.13.2
- optuna==2.10.0
- scikit-optimize==0.9.0
- Create recursive autoregressive forecasters from any regressor that follows the scikit-learn API
- Create multi-output autoregressive forecasters from any regressor that follows the scikit-learn API
- Grid search to find optimal hyperparameters
- Grid search to find optimal lags (predictors)
- Include exogenous variables as predictors
- Include custom predictors (rolling mean, rolling variance ...)
- Multiple backtesting methods for model validation
- Include custom metrics for model validation
- Prediction interval estimated by bootstrapping
- Get predictor importance
- Random search and bayesian search (using optuna or skopt) for hyperparameter optimization. Try
random_search_forecaster()
andbayesian_search_forecaster()
. -
ForecasterAutoregMultiOutput
has been renamed toForecasterAutoregDirect
. - Modeling multiple time series simultaneously. Try
ForecasterAutoregMultiSeries
and the new functions availables inmodel_selection_multiseries
. - Allow different transformations for each predictor (lags and exogenous).
- Bug fixes and performance improvements.
Try it:
pip install git+https://github.com/JoaquinAmatRodrigo/skforecast#master
Visit changelog to view all notable changes.
The documentation for the latest release is at skforecast docs .
Recent improvements are highlighted in the release notes.
English
-
Skforecast: time series forecasting with Python and Scikit-learn
-
Forecasting time series with gradient boosting: Skforecast, XGBoost, LightGBM and CatBoost
Español
-
Skforecast: forecasting series temporales con Python y Scikit-learn
-
Forecasting series temporales con gradient boosting: Skforecast, XGBoost, LightGBM y CatBoost
If you found skforecast useful, you can support us with a donation. Your contribution will help to continue developing and improving this project. Many thanks!
If you use this software, please cite it using the following metadata.
APA:
Amat Rodrigo, J., & Escobar Ortiz, J. skforecast (Version 0.5.0) [Computer software]
BibTeX:
@software{skforecast,
author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
license = {MIT},
title = {{skforecast}},
version = {0.5.0}
}
View citation file.
joaquinAmatRodrigo/skforecast is licensed under the MIT License, a short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code.